Artificial Intelligence in the News (March 16th, 2022)
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https://venturebeat.com/2021/11/15/astera-labs-announces-memory-acceleration-to-clear-datacenter-ai-ml-bottlenecks/
Do you think this technology would allow the use of clusters to handle larger data sets, thus reducing the overall cost of doing ML?
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https://medium.com/p/306fa7b7a80b
I believe a common misconception is that you only need to apply MLOps principles and tools if you are running hundreds of models. I'd argue it's not less important in a lot earlier stages of the model lifecycle.
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The [BigScience project](https://bigscience.huggingface.co) has just started the training of its main model and the training can be followed live here: https://twitter.com/BigScienceLLM and here: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
Here are more information on the model, dataset, engineering, training and hardware:
The model:
176B parameters decoder-only architecture (GPT-like)
70 layers - 112 attention heads per layers - hidden dimensionality of 14336 - 2048 tokens sequence length
ALiBi positional embeddings - GeLU activation function
Read more:
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-…
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In this video, I discuss ORQA which uses a retriever to find the right context from the entire Wikipedia and then uses an extractive QA model to give a final answer. We discuss the task setup, architecture, and loss function.
The video is part of 8 video series on Open domain question answering, how it is different from normal QA, the difference in loss formulations, and key papers on different Open-QA architectures.
I will really appreciate any feedback.
https://www.youtube.com/watch?v=9bL2VbwZ9G8
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Hi MachineLearning,
I would like to introduce a new concept of utilizing factorized optical flow maps as mid-level representations, for bridging the perception and the control modules in modular learning based robotic frameworks.
In the below video, we demonstrate the DRL agent is able to control itself by perceiving the factorized optical flow maps, and without bumping into the pedestrians in the urban environment based on Unity.
Hope you like the idea and enjoy the video!
The screenshot from the demo video
Demo video: https://youtu.be/Op4QRTJOGMY
More details here: https://arxiv.org/abs/2203.04927
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deepmind/mctx: Monte Carlo tree search in JAX (github.com)
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In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that […]
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MLK Visiting Professor S. Craig Watkins looks beyond algorithm bias to an AI future where models more effectively deal with systemic inequality.
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For context, I am a cofounder of Encord, a company building software to improve training data for computer vision.
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Hey everyone!
We just posted Part 2 of our Tutorial on Conformal Prediction and Distribution-Free Uncertainty Quantification on YouTube!
https://youtu.be/TRx4a2u-j7M
It focuses on conditional coverage and diagnostics to make sure your conformal procedure is working properly. It's slightly more advanced than the last one, but will leave you with a strong understanding of how to implement/evaluate conformal in code.
Let us know if you have any feedback by shooting me an email :)
Best,
Anastasios
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Wednesday night (3/16), 8-11 pm EDT, FSU professor and computer scientist Dr. Chris Mills will be the guest on Ask_a_Scientist_Gaming.
Chris’ research focus started in applications of machine learning to common software development tasks like concept location and traceability link recovery but has since broadened to applications of machine learning across many industries including finance and law. Current projects include building database-agnostic, natural language interfaces for question-and-answer systems with impedance reduction built from off-the-shelf object-relational mapping. With such an interface, users can directly answer questions and query data with no knowledge of a query language and no need to have custom reports constructed for each information need. Think “Jarvis,” but employees play the role of Iron Man at a bank… and a law firm…. and a hospital… and a university…. and the list goes on.
If you can’t make the live stream, feel free to leave your question in the comments and we will get them answered. Then follow up with our YouTube channel where we will post the video.
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Organizations use messaging platforms like Slack to bring the right people together to securely communicate with each other and collaborate to get work done. A Slack workspace captures invaluable organizational knowledge in the form of the information that flows through it as the users collaborate. However, making this knowledge easily and securely available to users […]
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Critical information can be scattered across multiple data sources in your organization, including sources such as Windows file systems stored on Amazon FSx for Windows File Server. You can now use the Amazon Kendra connector for FSx for Windows File Server to index documents (HTML, PDF, MS Word, MS PowerPoint, and plain text) stored in […]
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In this post, we demonstrate how to create an automated email response solution using Amazon Comprehend. Organizations spend lots of resources, effort, and money on running their customer care operations to answer customer questions and provide solutions. Your customers may ask questions via various channels, such as email, chat, or phone, and deploying a workforce […]
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We’ve released new versions of GPT-3 and Codex which can edit or insert content into existing text, rather than just completing existing text. These new capabilities make it practical to use the OpenAI API to revise existing content, such as rewriting a paragraph of text or refactoring code.
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Calculus for Machine Learning Crash Course. Get familiar with the calculus techniques in machine learning in 7 days. Calculus is […]
The post Calculus for Machine Learning (7-day mini-course) appeared first on Machine Learning Mastery.
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What is Data Fabric ?
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A machine-learning model for image classification that’s trained using synthetic data can rival one trained on the real thing, a study shows.
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Made a super tiny library that hashes your data and compares the hashes to determine if you have samples leaked into the other dataset.
Main usage is to add one line of code before your training loop as an extra check.
Useage is as easy as: python spills = check_spill(train_loader, test_loader)
Github: https://github.com/LaihoE/did-it-spill Currently only for PyTorch
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Hi folks,
SuperAnnotate is launching webinar series on automated computer vision pipelines, and the first episode is here for you to check out!
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This is a post co-written with Bernard Paques, CTO of Storm Reply, and Karl Herkt, Senior Strategist at Dassault Systèmes 3DExcite. While computer vision can be crucial to industrial maintenance, manufacturing, logistics, and consumer applications, its adoption is limited by the manual creation of training datasets. The creation of labeled pictures in an industrial context […]
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Are you aware of the technicalities involved in making Machine Learning models holistic, intuitive, and impactful? If not, you first need…
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A considerable part of the population uses mobile devices and computers to search data. They also store and perform data procedures. However, not everyone is aware of the relevance of data backup. Your crucial data is essential for anything that you do. It is here that technical support companies can help. Whether it’s your desktop… Read More »Why is Data Back-Up Necessary? The Benefits of Availing Technical Support
The post Why is Data Back-Up Necessary? The Benefits of Availing Technical Support appeared first on Data Science Central.
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In the past, Metadata Management is used to know how to use data catalog to find simple data or a book or a periodical in a library. However, today it is one of the most critical data practices for a successful organization dealing with data. With the rise of distributed architectures, including cloud & big… Read More »Why do you need a metadata management system? Definition and Benefits.
The post Why do you need a metadata management system? Definition and Benefits. appeared first on Data Science Central.
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Just wanted to ask anyone with experience working in the field of implicit neural represenations regarding the compute requirements you've experienced when developing models. Mainly looking in the domain of neural radiance fields (https://www.matthewtancik.com/nerf). I do have cluster access for evaluating projects that are more mature in the development pipeline, but wanted to gauge if anyone had any advice regarding what has worked when still in earlier development mainly when working on my standalone PC.
Thanks so much for any help!
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A well popularized article in Quanta magazine ask the question « Will Transformers Take Over Artificial Intelligence? ». Since having revolutionized NLP, attention is conquering computer vision and reinforcement learning. I find pretty unfortunate that the attention mechanism was totally eclipsed by Transformers which is just a funny name (animation movie/ toy) for self-attention architecture, although the Google's paper title on Transformers was «Attention is all you need».
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Ice protects the Earth layer and its oceans by acting as a shield. Excess heat is reflected into space by these dazzling white spots, keeping the Earth cold. Many glaciers throughout the world have been melting quickly since the early 1900s. Human actions cause this phenomenon. Carbon dioxide (CO2) and other greenhouse gas emissions have elevated temperatures since the industrial revolution.
Melting glaciers are a contributing factor in rising sea levels, which leads to an increase of coastal erosion and storm surge. Warmer air temperatures lead directly into more frequent storms like hurricanes or typhoons with stronger winds that cause even greater damage on land. Many cities are already planning to deal with long-term flooding, which may carry salt and moisture into houses and infrastructure, jeopardize drinking water and agriculture, and severely damaged ports.
Given the gravity of the problem, it is critical to understand how much and how quickly sea levels will rise. The projections in the existing predictive models made by scientists are pretty uncertain. Since the contribution from the southernmost continent is so unknown, governments worldwide must consider an unlimited number of scenarios when planning for the future.
A group of Stanford University scientists employed autonomous drone technology and machine learning approach to focus their efforts on discovering and gathering the most valuable data in Antarctica to increase our understanding of the processes that drive sea-level rise.
Continue Reading Our Summary on This Research From Stanford or checkout the HAI Report
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Hey My Reddit Fellows,
I just wanted to share a video series I am making about AGI, how to manage AGI Safety, and what the post singularity society will look like. Please subscribe to my channel, and let me know if you have any feedback and what topics you would like to see next!
►Playlist: https://youtube.com/playlist?list=PLb4nW1gtGNse4PA_T4FlgzU0otEfpB1q1
►AGI Existential Threat: https://youtu.be/V4iQP7VDMvI
►Life 3.0: https://youtu.be/aWlSwZKzmzY
►Dangers of AGI Sub Goals: https://youtu.be/_-tQH03rq4g
►How to Create an AGI: https://youtu.be/7OHhqli9oaA
Thank you!
Bill
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I recently had a long conversation with Tim Scarfe and Keith Duggar on Machine Learning Street Talk (MLST) about theory related to neural networks. I really believe we can make better machine learning algorithms and better guarantees if we uncover the right theoretical track. I would really appreciate hearing from you all in the community about this work, so I've written up this post to accompany the MLST video. Enjoy!
See the full interactive version of this post on my research page here.
Get the code for these experiments here.
Data Distributions and Initializing Neural Networks
Is it possible for us to make fixed-size multilayer perceptrons (MLP's) provably converge? It's been bothering me that initialization seems arbitrary and all the optimization algorithms produce different resu…
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https://nn.labml.ai/transformers/retro/model.html
This is an annotated (side-by-side notes) implementation of RETRO in PyTorch.
Retrieval Enhanced Transformer (RETRO) is 25X smaller than GPT-3 but has comparable performance. It uses chunks of similar text retrieved based on a frozen BERT model from a massive database (5 trillion tokens) to improve the performance of the model. Since the model can retrieve information from this large database it doesn't have to contain all the facts in the model weights.
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The news was announced here.
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Hi folks. I'm a new DL researcher who just began working at a startup that focuses on AI-based drug discovery. I'm afraid this is not the most suitable place to post this since this is more of an engineering idea, but I wanted to hear what you guys think about it and if you have any idea.
I don't know how many of you have encountered the same efficiency issue before, but I've repeatedly come across this theme while implementing my research ideas:
I have a dataset that consists of datapoints with non-uniform length along some dimensions (number of atoms in a molecule, number of amino acids in a protein etc.), and I want to perform numerical calculations (e.g. feed into a DL model) on a tensorized-batched form of them. The batching (turning them into a single tensor) would be an indispensi…
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Scientists conduct trial and error procedures which experimenting, that many times lear to freat scientific breakthroughs. Similarly, foundational research provides for developing large-scale AI systems theoretical insights that reduce the amount of trial and error required and can be very cost-effective.
Microsoft team tunes massive neural networks that are too expensive to train several times. For this, they employed a specific parameterization that maintains appropriate hyperparameters across varied model sizes. The used µ-Parametrization (or µP, pronounced “myu-P”) is a unique way to learn all features in the infinite-width limit. The researchers collaborated with the OpenAI team to test the method’s practical benefit on various realistic cases.
Studies have shown that training large neural networks because their behavior changes as they grow in size are uncertain. Many works suggest heuristics that attempt to maintain consistency in the activation scales at initialization. However, as training progresses, this uniformity breaks off at various model widths.
CONTINUE READING MY SUMMARY ON THIS RESEARCH
Paper: https://www.microsoft.com/en-us/research/uploads/prod/2021/11/TP5.pdf
Github:https://github.com/microsoft/mup
https://i.redd.it/wu93hpd7wvm81.gif
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Dears.
I have more than 5 years experience in machine learning and deep learning and recently have created a Youtube channel. https://www.youtube.com/channel/UCn9Rujwh7SfHF2RRvy_ks-g In the channel, I first explain a paper, then I implement/explain the code .
Please join, leave a comment, and share with your friends. You can also suggest any paper and I will add it to my list.
I am constantly trying to improve contents and quality.
Thanks.
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https://pytorch.org/blog/pytorch-1.11-released/
As a longtime TensorFlow user I've been meaning to switch to either JAX or PyTorch, thus I'm pretty intrigued by this.
In the past I've been having a hard time giving up tf.data's pretty elegant fluent interface for performant I/O and data preprocessing. Has anyone tried the new PyTorch equivalent? How does TorchData stack up?
And are there more things in JAX that functorch cannot express or will both autograd engines hit feature parity now-ish?
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Check to understand how image recognition technology works and why image detection revolutionizes business.
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Chemical engineers use neural networks to discover the properties of metal-organic frameworks, for catalysis and other applications.
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We’ve recently launched our machine learning profiler https://github.com/graphsignal/graphsignal to make ML profiling simple and usable. It automatically provides operation and kernel level statistics as well as detailed resource usage information necessary for making training and inference faster and more efficient.
More details and screenshots in the blog post https://graphsignal.com/blog/machine-learning-profiler-for-training-and-inference/.
I hope some of you find it useful. Any feedback is appreciated.
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Deep Learning Is Hitting a Wall: What would it take for artificial intelligence to make real progress?
Essay by Gary Marcus, published on March 10, 2022 in Nautilus Magazine.
Link to the article: https://nautil.us/deep-learning-is-hitting-a-wall-14467/
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For the 14th consecutive year, each Academy Award nominee for the Best Visual Effects used NVIDIA technologies. The 94th annual Academy Awards ceremony, taking place Sunday, March 27, has five nominees in the running: Dune Free Guy No Time to Die Shang-Chi and the Legend of the Ten Rings Spider-Man: No Way Home NVIDIA has Read article >
The post At the Movies: For 14th Year Running, NVIDIA Technologies Power All VFX Oscar Nominees appeared first on NVIDIA Blog.
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Say hello to tomorrow’s smart electric meter, literally. You can ask some next-generation home energy hubs questions, just like you do Alexa or Siri. Some devices, arriving this year, will display real-time simulations — vibrant as a video game — to show how you can lower your energy bill or reduce your carbon footprint. They’ll Read article >
The post Light Me Up: Innovators Redefine Energy Meters for a More Efficient Grid appeared first on NVIDIA Blog.
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The GeForce NOW RTX 3080 membership gives gamers unrivaled performance from the cloud – with latency so low that it feels just like playing on a local PC. Today, gamers can experience RTX 3080-class streaming at only $19.99 a month, thanks to GeForce NOW’s new monthly membership plans*. It’s a great chance to experience powerful Read article >
The post GeForce NOW RTX 3080 One-Month Memberships Now Available appeared first on NVIDIA Blog.
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In this post, we will demonstrate how to securely launch notebook instances in a private subnet of an Amazon Virtual Private Cloud (Amazon VPC), with internet access disabled, and to securely connect to Amazon Simple Storage Service (Amazon S3) using VPC endpoints. This post is for network and security architects that support decentralized data science […]
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Many software as a service (SaaS) providers across various industries are adding machine learning (ML) and artificial intelligence (AI) capabilities to their SaaS offerings to address use cases like personalized product recommendation, fraud detection, and accurate demand protection. Some SaaS providers want to build such ML and AI capabilities themselves and deploy them in a […]
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Amazon SageMaker Autopilot is an automated machine learning (AutoML) solution that performs all the tasks you need to complete an end-to-end machine learning (ML) workflow. It explores and prepares your data, applies different algorithms to generate a model, and transparently provides model insights and explainability reports to help you interpret the results. Autopilot can also […]
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In this post, we present a solution for digitizing transactional documents using Amazon Textract and incorporate a human review using Amazon Augmented AI (A2I). You can find the solution source at our GitHub repository. Organizations must frequently process scanned transactional documents with structured text so they can perform operations such as fraud detection or financial […]
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AWESOME MACHINE LEARNING
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It is no secret that a mobile app is among the most powerful tools at the disposal of the market and that too across all sectors. They not only empower companies with the ability to reach out to and engage with their customers all over the world but also deliver a powerful boost to their… Read More »Flutter vs Kotlin: Comparison of Mobile App Development Frameworks
The post Flutter vs Kotlin: Comparison of Mobile App Development Frameworks appeared first on Data Science Central.
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Looking to fill open tech positions with quality hires quickly? Like many other IT leaders, you may be facing the uphill task of finding skilled candidates for various tech positions. A report on the impact of technology predicts that in 2022, filling tech positions will remain the key challenge for 73 percent of IT leaders.… Read More »Fast-track The Way You Find Tech Talent with These IT Staffing Solutions
The post Fast-track The Way You Find Tech Talent with These IT Staffing Solutions appeared first on Data Science Central.
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Data management (DM) discussions can be frustrating because both those feeling the pain and the consultants who try to help them are–90+ percent of the time, it seems–still using the same old ways. Those ways only go so far, and won’t go any farther. That’s because those who reinforce the old ways assume that what… Read More »The long game: Desiloed systems and feedback loops by design (I of II)
The post The long game: Desiloed systems and feedback loops by design (I of II) appeared first on Data Science Central.
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Projects fail. There are many reasons why they do, but a surprising number of them come down to one or more variations of the “Wishful Thinking” theme. From a data science standpoint, this is usually referred to as making faulty assumptions, but the idea is the same. And with very few exceptions, the assumptions being… Read More »DSC Weekly Digest 08 March 2022: Beware of Wishful Thinking
The post DSC Weekly Digest 08 March 2022: Beware of Wishful Thinking appeared first on Data Science Central.
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See here for info.
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This guide is intended for anyone having zero or a small background in programming, maths, and machine learning. There is no specific order to follow, but a classic path would be from top to bottom. If you don't like reading books, skip it, if you don't want to follow an online course, you can skip it as well. There is not a single way to become a machine learning expert and with motivation, you can absolutely achieve it.
The video: https://youtu.be/RirEw-uaS_8?list=PLO4GrDnQanVfb6Ins6up1xScJHl8YuwPQ
The complete article: https://pub.towardsai.net/start-machine-learning-in-2020-become-an-expert-from-nothing-for-free-f31587630cf7
All the links on GitHub: https://github.com/louisfb01/start-machine-learning-in-2020
Artificial is a fantastic field, but it goes extremely fast. Don't miss out on the most important and exciting news by joining great communities, people, newsletters, and more you can all find in this guide!
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Amazon Comprehend is a natural language processing (NLP) service that uses machine learning (ML) to discover insights from text. As a fully managed service, Amazon Comprehend requires no ML expertise and can scale to large volumes of data. Amazon Comprehend provides several different APIs to easily integrate NLP into your applications. You can simply call […]
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Amazon SageMaker Autopilot automatically builds, trains, and tunes the best machine learning (ML) models based on your data, while allowing you to maintain full control and visibility. We have recently announced support for time series data in Autopilot. You can use Autopilot to tackle regression and classification tasks on time series data, or sequence data […]
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This is a guest post by Oliver Frost, data scientist at ImmoScout24, in partnership with Lukas Müller, AWS Solutions Architect. In 2010, ImmoScout24 released a price index for residential real estate in Germany: the IMX. It was based on ImmoScout24 listings. Besides the price, listings typically contain a lot of specific information such as the […]
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This post is authored by Satish Jha, Intelligent Automation Manager, Matt Docherty, Data Science Manager, Jayesh Muley, Associate Consultant and Tapan Vora, Rapid Prototyping, from ZS Associates. At ZS Associates, we do a significant amount of qualitative market research. The work involves interviewing relevant subjects (such as healthcare professionals and sales representatives) and developing bespoke […]
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Hey, machine learning experts!
I represent a community-driven open source project called MindsDB (see on GitHub). We need your feedback about our concept for doing machine learning using SQL! It is called AI Tables and aims to democratize machine learning for all who work with data. There's an article on Medium with SQL commands examples.
Please share your thoughts about it. Your true opinions will be a great contribution towards what a team of 25+ people are working on in the last 3 years!
Thanks in advance!
Costa
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The report[https://www.theverge.com/2017/12/5/16737224/global-ai-talent-shortfall-tencent-report] by The Verge says "Tencent says there are only 300,000 AI engineers worldwide in 2017", Personally, I'm curious how many machine learning engineers in 2022, anyone have any idea?
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WHAT IS HUMAN-MACHINE INTERFACE (HMI)?
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https://youtu.be/6dvcYx9hcbE
This is an in-depth paper review, followed by an interview with the papers' authors!
Society is ruled by norms, and most of these norms are very useful, such as washing your hands before cooking. However, there also exist plenty of social norms which are essentially arbitrary, such as what hairstyles are acceptable, or what words are rude. These are called "silly rules". This paper uses multi-agent reinforcement learning to investigate why such silly rules exist. Their results indicate a plausible mechanism, by which the existence of silly rules drastically speeds up the agents' acquisition of the skill of enforcing rules, which generalizes well, and therefore a society that has silly rules will be better at enforcing rules in general, leading to faster adapt…
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Video #1
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View Poll
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This is a guest post authored by Andrew Masek, Software Engineer at The Barcode Registry and Erik Quisling, CEO of The Barcode Registry. Product counterfeiting is the single largest criminal enterprise in the world. Growing over 10,000% in the last two decades, sales of counterfeit goods now total $1.7 trillion per year worldwide, which is […]
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🔥 I received several messages about the benefits of joining FAANG and similar companies and startups in the context of Data Science…
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If you want to help make a difference in the world, we are looking for collaborators in a unique study of butterflies.
In collaboration with Zooniverse and Microsoft’s AI for Earth. We’re using artificial intelligence to extract wing traits from millions of digitized museum specimens. Wing size and color are important for thermoregulation, dispersal ability, and thus responses to land use and climate change.
The integration of emerging technology with digitization of museum specimens around the world opens up new and exciting research questions.
Join the mission.
https://collaboratory.ist/job/ai-for-butterflies.html/
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Ever wondered how OCR engines extract information, and structure it? Here is an explainer on one of the most successful deep learning models that is able to achieve this. https://nanonets.com/blog/layoutlm-explained/
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This article focuses on what is happening behind the execution of a Tensor in the deep learning framework OneFlow. It takes the operator oneflow.relu as an example to introduce the Interpreter and VM mechanisms that need to be relied on to execute this operator.
article: https://oneflow2020.medium.com/the-execution-process-of-a-tensor-in-a-deep-learning-framework-a4d853645d5b
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For more information, https://stackoverflow.com/questions/71387216/how-do-i-train-a-from-scratch-image-recognition-neural-network.
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Hey My Reddit Fellows,
I just wanted to share a video series I am making about AGI, how to manage AGI Safety, and what the post singularity society will look like. I am looking to help share some of the most important thoughts from AI safety experts to ensure that current ML practitioners and next generation will have the important knowledge to build a path to a prosperous future together. Please check it out, subscribe to my channel, and let me know if you have any feedback and what topics you would like to see next!
https://youtube.com/playlist?list=PLb4nW1gtGNse4PA_T4FlgzU0otEfpB1q1
AGI Existential Threat: https://youtu.be/V4iQP7VDMvI
Life 3.0: https://youtu.be/aWlSwZKzmzY
Dangers of AGI Sub Goals: https://youtu.be/_-tQH03rq4g
How to Create an AGI: https://youtu.be/7OHhqli9oaA
Thank you!
Bill
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Moving enterprise content management to the cloud comes with impressive business benefits — operating costs are reduced, capital…
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What is Deep Learning ?
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In part 1: A gentle introduction to positional encoding in transformer models, we discussed the positional encoding layer of the […]
The post The Transformer Positional Encoding Layer in Keras, Part 2 appeared first on Machine Learning Mastery.
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I wrote a step-wise tutorial to demonstrate the steps required to deploy an ML model using GCP's Google AI Platform and using Streamlit to access the model API through a UI.
Check out the blog here - https://shreyansh26.github.io/post/2022-03-06_model_deployment_using_gcp_google_ai_platform/
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Machine learning (ML) is becoming a more critical application for developers since it allows them to train models that can do various prediction-based activities. You may have had to develop a complicated rules engine in the past, relying on mathematical methodologies to offer the essential statistical models.
Predictions are what we call machine learning (ML) outputs, although they may be anything. They can be detected items if you use computer vision. They’re intent or translations if you’re utilizing a language model. Whatever the result, it’s a statistically weighted answer with a confidence level that may be used to verify any results.
Working with machine learning has two components. If you have a prebuilt model, you may use a REST API to interact with its predictions on a cloud platform like Azure ML or export it in the widely accepted ONNX (Open Neural Network Exchange) format and run it on a PC using tools like WinML. That’s the simple part; training and evaluating a model is complex. That method necessitates a large amount of data to be verified and tagged. Also, there is a substantial amount of computing on a CPU or on a GPGPU (general-purpose GPU).
Continue Reading My Summary on PyTorch-DirectML Release-2
Download: https://pypi.org/project/pytorch-directml/
Github: https://github.com/microsoft/DirectML
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What would an AI who's never seen or heard of golf courses do when shown a list of real golf course names and challenged to generate more?
When Jeff Kissel sent me 15,626 existing golf course names from the National Course Rating Database, I thought I might
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AI Weirdness: the strange side of machine learning
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About a year ago, I started the pianist AI project with the aim of having an AI model that can generate piano pieces. Although the optimization is still in process, today, finally it seems the model has learned the basic concepts.
I have named the first piece of Level 7: Peace> https://youtu.be/rLW3KwCG41M
With the hope of better tomorrow….
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Deep generative models have produced realistic samples in a variety of domains, including image and audio. Video generation has recently emerged as the next issue for deep generative models, prompting a long line of research to learn video distribution.
Despite their efforts, there is still a big gap between large-scale real-world recordings and simulations. The intricacy of video signals, which are continuously coupled across spatiotemporal directions, contributes to the difficulty of video creation. Specifically, most previous works have modeled the video as a 3D grid of RGB values, i.e., a succession of 2D images, using discrete decoders such as convolutional or autoregressive networks. However, because of the cubic complexity, such discrete modeling limits the scalability of created movies and misses the intrinsic continuous temporal dynamics.
Continue Reading My Article Summary On This Research
Paper: https://openreview.net/pdf?id=Czsdv-S4-w9
Github: https://github.com/sihyun-yu/digan
Project: https://sihyun-yu.github.io/digan/
https://reddit.com/link/t7f7ti/video/nicll8ujvll81/player
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This is part 1 in a multi-part series on the value-realizing, collaborative power of decisions. My mom used to say, “If it was a snake, you’d be dead”. And the reason that I say that, is that organizations are seeking a collaborative value driver that can 1) align the organization around the economic power of… Read More »Decisions Part 1: Creating an AI-driven Decision Factory
The post Decisions Part 1: Creating an AI-driven Decision Factory appeared first on Data Science Central.
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Hello everyone. I am excited about the invitation to do an AMA here. It's my first AMA on reddit, and I will be trying my best! I recently wrote the "Machine Learning with Pytorch and Scikit-Learn" book and joined a startup(Grid.ai) in January. I am also an Assistant Professor of Statistics at the University of Wisconsin-Madison since 2018. Btw. I am also a very passionate Python programmer and love open source.
Please feel free to ask me anything about my book, working in industry (although my experience is still limited, haha), academia, or my research projects. But also don't hesitate to go on tangents and ask about other things -- this is an ask me anything after all (... topics like cross-country skiing come to mind).
EDIT:
Thanks everyone for making my first AMA here a really fun experience! Unfortunately, I have to call it a day, but I had a good time! Thanks for all the good questions, and sorry that I couldn't get to all of them!
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MEng graduate students engage with IBM to develop their research skills and solutions to real-world problems.
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Whether you’re allocating resources more efficiently for web traffic, forecasting patient demand for staffing needs, or anticipating sales of a company’s products, forecasting is an essential tool across many businesses. One particular use case, known as cold start forecasting, builds forecasts for a time series that has little or no existing historical data, such as […]
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Real estate businesses have existed for many years and will almost definitely continue to succeed.
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The deployment of powerful AI systems has enriched our understanding of safety and misuse far more than would have been possible through research alone. Notably:
API-based language model misuse often comes in different forms than we feared most.
We have identified limitations in existing language model evaluations that we are
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Call for expressions of interest to study the economic impacts of Codex.
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https://www.immunai.com/press/advancements-in-gene-regulatory-networks-immunais-third-symposium
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We have two talks next week (week of 7 Mar.) for our upcoming webinar series about the intersection of Bayesian inference and causal inference. Our speakers will help us understand how we can use these two frameworks in order to solve applied problems, and will consider if these different frameworks are in conflict or are complimentary. The intended audience is machine learning practitioners and statisticians from academia and industry.
Upcoming talks, with Zoom registration links:
7 March
Andrew Gelman - Bayesian Methods in Causal Inference and Decision Making
Consider the problem of A/B testing (that is, an experiment or observational study designed to estimate the effect of some exposure or treatment). The basic data analysis workflow is to start by comparing the average outcomes…
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A new MIT-wide effort launched by the Institute for Data, Systems, and Society uses social science and computation to address systemic racism.
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At the 2021 AWS re:Invent conference in Las Vegas, we demoed Read For Me at the AWS Builders Fair—a website that helps the visually impaired hear documents. For better quality, view the video here. Adaptive technology and accessibility features are often expensive, if they’re available at all. Audio books help the visually impaired read. Audio […]
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A new month means a whole new set of games coming to GeForce NOW. Members can look forward to 27 titles joining the GeForce NOW library in March, including day-and-date releases like Shadow Warrior 3, with support for NVIDIA DLSS. Bring a Katana to a Gunfight Shoot, slash and slide into Shadow Warrior 3, new Read article >
The post GFN Thursday Marches Forward With 27 Games Coming to GeForce NOW This Month appeared first on NVIDIA Blog.
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This is a beefed version of SEER which was released a year ago, scaled from 1B to 10B parameters, showing improved generalization on different tasks.
Also, the self supervised learning (SSL) allowed for a better coverage of the world , thus reducing bias from labelled datasets mostly originating from specific countries (e.g. US).
Results look very nice.
More details in their post.
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Hi folks, we recently implemented multi-touch attribution using shapley and markov chain values at our org, and I wrote a blog post about how we implemented it using a mix of tools (primarily dbt, sagemaker, and our internal tools). I am sharing it here hoping people might find it interesting. Do let me know if you have any questions/feedback/suggestions.
https://www.rudderstack.com/blog/from-first-touch-to-multi-touch-attribution-with-rudderstack-dbt-and-sagemaker/
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Are there any pytorch libraries to do benchmarking of domain adaptation methods for audio/speech tasks? Something like the Transfer Learning Library (https://github.com/thuml/Transfer-Learning-Library/) for images.
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Programming is way more fun when you learn/work with someone. Help each other, ask questions, brainstorm, etc. There is just so much benefit to joining a community when you are in this field, especially when you cannot find the question you are looking for on stack overflow! 😉
This is the same thing with AI, and it is why, nearly two years ago now, we created a Discord server where anyone learning or working in the field could come and share their projects, learn together, work together, and much more. The community has now over 22'000 members, which is just unbelievable! We are so glad to see it growing and especially to see everyone so active.
We would love for anyone to join and exchange with us, especially if you are willing to give some of your precious time to share your knowledge and help other people.
We have special events and projects for the community as well as cool offers and giveaways, such as an NVIDIA RTX 3080Ti giveaway running right now in collaboration with NVIDIA for the GTC event for the community members! (check out the #announcement channel for more information about this ;) )
Come join us if you are in the field of AI !
https://discord.gg/learnaitogether
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Imagine walking through the bustling streets of London’s Piccadilly Circus, when suddenly you’re in a tropical rainforest, surrounded by vibrant flowers and dancing butterflies. That’s what audiences will see in the virtual world of The Green Planet AR Experience, an interactive, augmented reality experience that blends physical and digital worlds to connect people with nature. Read article >
The post Beyond Be-leaf: Immersive 3D Experience Transports Audiences to Natural Worlds With Augmented Reality appeared first on NVIDIA Blog.
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The very thing that makes the internet so useful to so many people — the vast quantity of information that’s out there — can also make going online frustrating. There’s so much available that the sheer volume of choices can be overwhelming. That’s where recommender systems come in, explains NVIDIA AI Podcast host Noah Kravitz. Read article >
The post Podsplainer: What’s a Recommender System? NVIDIA’s Even Oldridge Breaks It Down appeared first on NVIDIA Blog.
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The Social and Ethical Responsibilities of Computing publishes a collection of original pedagogical materials developed for instructional use on MIT OpenCourseWare.
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Researchers find similarities between how some computer-vision systems process images and how humans see out of the corners of our eyes.
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In the presence of extrinsic rewards, Deep Reinforcement Learning (RL) is a strong strategy for tackling complex control tasks. Playing video games with pixels, mastering the game of Go, robotic mobility, and dexterous manipulation policies are all examples of successful applications.
While effective, the above advancements resulted in agents that were unable to generalize to new downstream tasks other than the one for which they were trained. Humans and animals, on the other hand, can learn skills and apply them to a range of downstream activities with little supervision. In a recent paper, UC Berkeley researchers aim to teach agents with generalization capabilities by efficiently adapting their skills to downstream tasks.
Continue reading my summary on this paper
Paper | Github
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What is Stroke ?
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The importance of set pieces in football (or soccer in the US) has been on the rise in recent years: now more than one quarter of all goals are scored via set pieces. Free kicks and corners generally create the most promising situations, and some professional teams have even hired specific coaches for those parts […]
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In football, as in many sports, discussions about individual players have always been part of the fun. “Who is the best scorer?” or “Who is the king of defenders?” are questions perennially debated by fans, and social media amplifies this debate. Just consider that Erling Haaland, Robert Lewandowski, and Thomas Müller alone have a combined […]
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How to utilize ML tools for gene regulatory networks and perturbation predictions: https://www.immunai.com/press/advancements-in-gene-regulatory-networks-immunais-third-symposium
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Veritasium posted a great video on the upcoming analog computers being used for neural networks as an alternative to digital computers.
https://www.youtube.com/watch?v=GVsUOuSjvcg&ab_channel=Veritasium
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Hi everyone!
I created RasgoQL as an open source python package for building dbt-compatible SQL in a pandas-like syntax. It’s already saved me hours of writing CTEs (common table expressions) in SQL, and I hope you’ll give it a try so it can save you time too.
You can check it out here: https://github.com/rasgointelligence/RasgoQL
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Synthetic datasets are computer-generated samples with the same statistical characteristics as the samples from the original dataset. Synthetic datasets are becoming common to train AIs in areas where real data is scarce or too sensitive to use, as in the case of medical records or personal financial data. I was involved in textual data augmentation for my thesis.
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Sponsored Post Me, a data scientist, and Jupyter notebooks. Well, our relationship started back then when I began to learn […]
The post Data Science Notebook Life-Hacks I Learned From Ploomber appeared first on Machine Learning Mastery.
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Serialization refers to the process of converting a data object (e.g. Python objects, Tensorflow models) into a format that allows […]
The post A Gentle Introduction to Serialization for Python appeared first on Machine Learning Mastery.
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We’re moving at the Cagle house and we’re discovering that, after eight years of living at the same place, one family can collect a lot of crap. The issue came up, in discussions with my spouse, that my mother-in-law had no sense of organization — which seemed odd because my wife’s mother was the kind… Read More »DSC Weekly Digest 01 March 2022: Taxonomists Classify, Ontologists Conceptualize
The post DSC Weekly Digest 01 March 2022: Taxonomists Classify, Ontologists Conceptualize appeared first on Data Science Central.
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The last few years have seen rapid development in the field of natural language processing (NLP). While hardware has improved, such as with the latest generation of accelerators from NVIDIA and Amazon, advanced machine learning (ML) practitioners still regularly run into issues scaling their large language models across multiple GPU’s. In this blog post, we […]
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Ambarella builds computer vision SoCs (system on chips) based on a very efficient AI chip architecture and CVflow that provides the Deep Neural Network (DNN) processing required for edge inferencing use cases like intelligent home monitoring and smart surveillance cameras. Developers convert models trained with frameworks (such as TensorFlow or MXNET) to Ambarella CVflow format […]
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Deploying and managing machine learning (ML) models at the edge requires a different set of tools and skillsets as compared to the cloud. This is primarily due to the hardware, software, and networking restrictions at the edge sites. This makes deploying and managing these models more complex. An increasing number of applications, such as industrial […]
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Can something like that be done? For example, the Kaluza-Klein theory was able to derive various existing physic equations using an 5 by 5 matrix. My idea comes from the first 4 minutes of this video https://www.youtube.com/watch?v=mmtLgYVEuJs
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GauGAN, an AI demo for photorealistic image generation, allows anyone to create stunning landscapes using generative adversarial networks. Named after post-Impressionist painter Paul Gauguin, it was created by NVIDIA Research and can be experienced free through NVIDIA AI Demos. How to Create With GauGAN The latest version of the demo, GauGAN2, turns any combination of Read article >
The post What Is GauGAN? How AI Turns Your Words and Pictures Into Stunning Art appeared first on NVIDIA Blog.
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Researchers surveyed 100 high-performing companies to determine which of them are leading adopters of machine intelligence and data analytics, and how they succeed.
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A new technique boosts models’ ability to reduce bias, even if the dataset used to train the model is unbalanced.
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For anyone curious. Announcement on Twitter: https://twitter.com/sirbayes/status/1498402522511253510
Download link: https://probml.github.io/pml-book/book2.html
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Hi, after months of closed beta I'm launching today a free, open source IDE for PyTorch called TorchStudio. It aims to greatly simplify researches and trainings with PyTorch and its ecosystem, so that most tasks can be done visually in a couple clicks. Hope you'll like it, I'm looking forward to feedback and suggestions :)
-> https://torchstudio.ai
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See here: https://cltc.berkeley.edu/reward-reports/
The authors propose a new kind of AI documentation, Reward Reports, that track what an RL system is optimizing for over time as it learns the dynamics of a domain and (as the case may be) actively reshapes them to conform to its specification.
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Every piece of software and code contains flaws. While some of these flaws are minor and simply impair an application’s functioning, others have the potential to compromise its security. It is important to find and remedy these security flaws for application security.
Code scanning is one such framework that now uses machine learning for detecting potential security flaws in software that identifies vulnerabilities and corrects them before they are released into production, reducing the security risks they offer. Continue reading our summary or you can also read Github blog
https://reddit.com/link/t37lvz/video/wsiqmbaxbik81/player
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Why do data silos, and now analytic silos, continue to exist? It can’t be due to technical issues. Data silos appeared in the 1990s when we were trying to make Relational Data Base Management Systems (RDMBS) – that were architected for single-record transaction processing – perform massive table scans to identify the trends, patterns, and… Read More »Abundance Mentality is Key to Exploiting the Economics of Data
The post Abundance Mentality is Key to Exploiting the Economics of Data appeared first on Data Science Central.
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When looking into Machine Learning models, I came across something that confuses me:
On the one hand "model" is referred to (e.g. by Google) as something you pick before you start the training process, meaning the raw mathematical method (e.g. linear regression).
On the other hand "model" is referred to (e.g. by Google) as the fully trained mathematical method, so the mapping from input to output that you get after training (e.g. the result of conducting a linear regression on the training data).
Is both correct? If not, how would you call the remaining thing from the two options above?
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https://www.youtube.com/watch?v=sKhCKWR8OwI&list=PLhCQIYxdniNtEto-CO33yVb0ah7yKOVV5&index=5
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AMA friday morning EST with Sebastian Raschka /u/seraschka author of Machine Learning with Pytorch and Scikit-Learn Book
It is on goodreads here
https://www.goodreads.com/book/show/60098440-machine-learning-with-pytorch-and-scikit-learn?from_search=true&from_srp=true&qid=MNIHuvctFr&rank=1
And a link to the code and such here
https://www.reddit.com/r/learnmachinelearning/comments/t1gqqe/machine_learning_with_pytorch_and_scikitlearn_book/
Ask him questions about his new book, academic research, or his job at http://Grid.ai
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See links attached for the two specifically the cpn results:
Poseformer - https://github.com/zczcwh/PoseFormer VideoPose - https://github.com/facebookresearch/VideoPose3D
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KServe (originally known as KFServing) provides a Kubernetes Custom Resource Definition for serving machine learning (ML) models on arbitrary frameworks. It is now the latest Incubation Project of the LF AI & Data Foundation.
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Note: I have not, personally, used this package.
The package is developed by scientists at Argonne National lab.
It sounds very interesting and uses bayesian optimization to explore the parameter space for ML model configuration (no. of neurons, loss, optimizer etc).
Edit: link thanks to u/ploomber-io
https://github.com/deephyper/deephyper
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You can read about here implemented in TensorFlow 2.8, trained in tf.GradientTape() API.
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A new machine-learning technique could pinpoint potential power grid failures or cascading traffic bottlenecks in real time.
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Hi
We have a very friendly and supportive wellness bot called Hawai. You can share about everyday stuffs and get tips on feeling good and happy. Give it a shot! Spread the word, especially given that millions are affected and feel so isolated!!! You can help just by sharing in social media and among your friends!!! Thanks!!!
Start a chat with Hawai - Health and wellbeing AI on Chai
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https://thegradient.pub/gradient-prize/
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A new methodology simulates counterfactual, time-varying, and dynamic treatment strategies, allowing doctors to choose the best course of action.
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This is a guest post by Kustomer’s Senior Software & Machine Learning Engineer, Ian Lantzy, and AWS team Umesh Kalaspurkar, Prasad Shetty, and Jonathan Greifenberger. In Kustomer’s own words, “Kustomer is the omnichannel SaaS CRM platform reimagining enterprise customer service to deliver standout experiences. Built with intelligent automation, we scale to meet the needs of […]
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Predictive maintenance can be an effective way to prevent industrial machinery failures and expensive downtime by proactively monitoring the condition of your equipment, so you can be alerted to any anomalies before equipment failures occur. Installing sensors and the necessary infrastructure for data connectivity, storage, analytics, and alerting are the foundational elements for enabling predictive […]
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Amazon SageMaker is a managed service that makes it easy to build, train, and deploy machine learning (ML) models. Data scientists use SageMaker training jobs to easily train ML models; you don’t have to worry about managing compute resources, and you pay only for the actual training time. Data ingestion is an integral part of […]
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The intersection of DI and AI: Drug information refers to the discovery, use, and management of healthcare and medical information. Healthcare providers have many challenges associated with drug information discovery, such as intensive time involvement, lack of accessibility, and accuracy of reliable data. The average clinical query requires a literature search that takes an average of 18.5 hours. In addition, drug information often lies in disparate information silos, behind pay walls and design walls, and quickly becomes stale.
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We have three talks next week (week of 28 Feb.) for our upcoming webinar series about the intersection of Bayesian inference and causal inference. Our speakers will help us understand how we can use these two frameworks in order to solve applied problems, and will consider if these different frameworks are in conflict or are complimentary. The intended audience is machine learning practitioners and statisticians from academia and industry.
Upcoming talks, with Zoom registration links:
28 Feb.
Christopher Sims - Large Parameter Spaces and Weighted Data: A Bayesian Perspective
Bayesian analysis can suggest ignoring sampling weights, even in contexts where popular estimation methods like Horvitz-Thompson or “doubly robust” estimates do use the weights. Weights are sometimes treated in …
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Hello here 👋
I just posted the second blog post of a series about Sentiment Analysis: https://medium.com/besedo-engineering/sentiment-analysis-part-2-how-to-choose-pre-annotated-datasets-for-sentiment-analysis-d79736e8c147
This blog post is about how to choose the perfect dataset for your Sentiment Analysis project, which is one of the most important part of a project. I hope you'll like it! 🙌
Any feedback is appreciated 🙏
Bonus: There are still two blog posts on this series!
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https://www.youtube.com/watch?v=IGOuV6UyQ1Q&list=PLhCQIYxdniNuu422uTU91PRHoj3gxF0uD&index=3
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Hi All!
I was wondering what are some of the ways that people use to automate their notebook works?
The main challenges I had in mind are version control, monitoring, better collaboration within the team, standardization across teams, and fast cloud deployments.
Disclosure, I'm part of the Ploomber team (an open source framework) and it solves most if not all of those problems.
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The Data Centric approach to building AI systems focuses on data instead of code.
We thought it would be cool if there was a central repository of all things Data Centric AI, so we set out to build one. We have put together a list of research papers and open-source tools on Data Centric AI, that we think you will find useful.
https://mindkosh.com/data-centric-ai/research-papers.html
https://mindkosh.com/data-centric-ai/open-source-tools.html
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According to the New-York Times, using machine learning, stylometry, and statistics on Q texts, two separate teams of NLP researchers from France and Swiss have identified the same two men as likely authors of messages that fueled the QAnon movement. First the initiator, Paul Furber, a South African software developer and then Ron Watkins took over, who operated 8chan website where the Q messages began appearing in 2018 and is now running election for Republican in Arizona.
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The development of ecologically acceptable biochemical substitutes for industrial processes might be accelerated thanks to nature’s molecular machinery.
Enzymes are the master accelerators of nearly every activity in the human body, helping with everything from digestion to the breakdown of hazardous chemicals and even DNA replication. Enzymes’ relevance extends beyond biology; they’re also utilized to make industrial chemical processes more environmentally friendly by reducing energy consumption and the number of harmful solvents needed in their production. The enzyme Xylanase treatment in paper manufacture, for example, has been demonstrated to reduce chlorine usage by 15% and toxic adsorbable organic halides (a chlorine byproduct) by 25% when producing white paper for printing or use in notebooks.
You can continue reading this article and tell us your feedback.
Github: https://github.com/rxn4chemistry/biocatalysis-model
Project: https://rxn.res.ibm.com/
Paper: https://www.nature.com/articles/s41467-022-28536-w.pdf
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Humans have the fundamental cognitive ability to perceive the environment through multimodal sensory signals and utilize this to accomplish a wide variety of tasks. It is crucial that an autonomous agent can similarly perceive the underlying state of an environment from different sensors and appropriately consider how to accomplish a task. For example, localization (or […]
The post COMPASS: COntrastive Multimodal Pretraining for AutonomouS Systems appeared first on Microsoft Research.
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Within the Mogao Caves, a cultural crossroads along what was the Silk Road in northwestern China, lies a natural reserve of tens of thousands of historical documents, paintings and statues of the Buddha.
The post Meet the Omnivore: 3D Creator Makes Fine Art for Digital Era Inspired by Silk Road Masterpieces appeared first on NVIDIA Blog.
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There’s no question that bad data hurts the bottom line. Bad customer data costs companies six percent of their total sales, according to a UK Royal Mail survey. The UK Government’s Data Quality Hub estimates organizations spend between 10% and 30% of their revenue tackling data quality. For multi-billion-dollar companies, that can easily be hundreds of millions of dollars… Read More »Data Observability Goes Far Beyond Data Quality Monitoring and Alerts
The post Data Observability Goes Far Beyond Data Quality Monitoring and Alerts appeared first on Data Science Central.
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Why do data silos, and now analytic silos, continue to exist? It can’t be due to technical issues. Data silos appeared in the 1990s when we were trying to make Relational Data Base Management Systems (RDMBS) – that were architected for single-record transaction processing – perform massive table scans to identify the trends, patterns, and… Read More »Abundance Mentality is Key to Exploiting the Economics of Data
The post Abundance Mentality is Key to Exploiting the Economics of Data appeared first on Data Science Central.
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As the demands of the modern market continue to evolve, more and more companies are starting to realize that their current infrastructure is not suited to keep up with the market and its requirements. And cloud management refers to the exercise of control over public, private, or hybrid cloud infrastructure with the right resources and… Read More »Top Best Practices to Keep in Mind for Azure Cloud Migration
The post Top Best Practices to Keep in Mind for Azure Cloud Migration appeared first on Data Science Central.
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An important issue for people developing ML-Models:
These biases affect belief formation, reasoning processes, business and economic decisions, and human behavior in general.
I've compiled a list (pdf) of over 150 biases (mainly from Wikipedia). Maybe this is useful for some.
The pdf can be downloaded for free here: A List of over 150 Biases (Belief, decision-making & behavioral, Social, Memory) (Medium)
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I just included 3 anecdotal stories in this article about machine learning model misinterpretation. Do you have any stories you've heard about model misinterpretation?
https://medium.com/codex/why-you-should-think-critically-about-your-machine-learning-model-outputs-864bc7d80709
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Hey guys, below I compiled a list of some relatively successful ML companies which also use various components of blockchain technology (aka cryptography). Feel free to comment on other companies which I might have left out:
https://www.linkedin.com/posts/xs94_ai-blockchain-machinelearning-activity-6901922373159710721-s_d_
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video!
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In this video, someone mentioned that he thinks self-supervised learning could solve RL problems. And on his Facebook page, he had some posts that look like RL memes.
What do you think?
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With more than 11,000 stores across Thailand serving millions of customers, CP All, the country’s sole licensed operator of 7-Eleven convenience stores, recently turned to AI to dial up its call centers’ service capabilities. Built on the NVIDIA conversational AI platform, the Bangkok-based company’s customer service bots help call-center agents answer frequently asked questions and Read article >
The post Talking the Talk: Retailer Uses Conversational AI to Help Call Center Agents Increase Customer Satisfaction appeared first on NVIDIA Blog.
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This is, without a doubt, the best time to jump into cloud gaming. GeForce NOW RTX 3080 memberships deliver up to 1440p resolution at 120 frames per second on PC, 1600p and 120 FPS on Mac, and 4K HDR at 60 FPS on NVIDIA SHIELD TV, with ultra-low latency that rivals many local gaming experiences. Read article >
The post How to Make the Most of GeForce NOW RTX 3080 Cloud Gaming Memberships appeared first on NVIDIA Blog.
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A well-popularized article in Quanta magazine about a scientific paper presented last December at NeurIPS, by Sébastien Bubeck of Microsoft Research and Mark Sellke of Stanford University provided a mathematical proof of why overparametrization (neural networks with more parameters than the number of training samples) is necessary to big neural network to learn well. The short answer is ROBUSTNESS...
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A model’s ability to generalize is influenced by both the diversity of the data and the way the model is trained, researchers report.
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Hi there, many of you have probably been aware of the whole twitter drama about AI consciousness, but if not you may find this write up about it interesting - Neural nets are not "slightly conscious," and AI PR can do with less hype . It's mostly a recap, but it does include a bunch of fun meme replies to the whole thing that you might enjoy even if you're aware of this whole thing.
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We posted our VAD demo here a while ago. Here's a follow-up article on The Gradient, where we attempt to explain:
Which values we did pursue;
Why we decided to create our own VAD;
Which criteria and metrics we optimized;
A brief overview of what is available in general;
How it compares with well-established and similar class solutions.
Links:
The article
The VAD is always available on Github
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Autonomous checkout is a rapidly advancing technology that has the potential to transform the way people shop in physical establishments. It frequently uses cameras and other sensors to gain a sense of a shopping environment and make a final conclusion about what a buyer buys. In systems where cameras are the only sensors available, computer vision is critical for comprehending this data. While vision-only autonomous checkout is still a relatively new concept, no benchmarks or new tasks have emerged.
In a recent study, researchers from Stony Brook University and Standard Cognition hypothesized that understanding retail settings necessitates not only domain-specific data but also a new computer vision task that detects changes in retail sceneries over time. Thus, they provide a new dataset, StandardSim, as well as a novel goal for detecting changes in retail scenarios over time in this study.
Quick Read: https://www.marktechpost.com/2022/02/19/stony-brook-university-researchers-introduce-standardsim-a-large-scale-photorealistic-synthetic-dataset-featuring-annotations-for-semantic-segmentation-instance-segmentation-depth-estimation-a/
Paper: https://arxiv.org/pdf/2202.02418.pdf
Github: https://github.com/standard-ai/Standard-Sim
Project: https://standard-ai.github.io/Standard-Sim/
https://i.redd.it/m9esh8383xi81.gif
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Currently I use TensorflowOnSpark frame to train and predict model. When prediction, I have billions of samples to predict which is time-consuming. I wonder if there is some good practices on this.
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When video chatting with colleagues, coworkers, or family, many of us have grown accustomed to using virtual backgrounds and background filters. It has been shown to offer more control over the surroundings, allowing fewer distractions, preserving the privacy of those around us, and even liven up our virtual presentations and get-togethers. However, Background filters don’t always work as expected or perform well for everyone.
Image segmentation is a computer vision process of separating the different components of a photo or video. It has been widely used to improve backdrop blurring, virtual backgrounds, and other augmented reality (AR) effects. Despite advanced algorithms, achieving highly accurate person segmentation seems challenging. Continue Reading
Meta Source: https://ai.facebook.com/blog/creating-better-virtual-backdrops-for-video-calling-remote-presence-and-ar/
https://reddit.com/link/swkjkr/video/o57elp9gzui81/player
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So one of the clubs in my college has done something quite impossible by roping in Sir Geoffrey Hinton, a living legend! Would be great if y'all join in, it's actually kinda a one-time opportunity to interact with him live, so do not miss it!
Link for RSVP
Youtube Link
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https://www.youtube.com/watch?v=IGOuV6UyQ1Q&list=PLhCQIYxdniNuu422uTU91PRHoj3gxF0uD&index=3
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Engineers build a lower-energy chip that can prevent hackers from extracting hidden information from a smart device.
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Release notes are here: https://github.com/openai/gym/releases/tag/0.22.0
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Hi all,
Tensorboard is a nice tool to visualize experiment results. However, it is quite difficult to parse the event logs into raw data for scientific plotting. So, I've created a PyPI package to make the parsing process as simple as possible (2 lines of code) and can be installed from pip: pip install tbparse. It supports reading event files generated by PyTorch/TensorFlow/Keras/TensorboardX and can parse most of the event types supported by tensorboard.
The package is open source (https://github.com/j3soon/tbparse) and the usages are documented in detail (https://tbparse.readthedocs.io/en/latest/). My friends and I have been using it for a while and find it very convenient, so I think some of you may benefit from it. I would be happy to hear your feedback and feature requests.
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What's the difference between the terms "metaheuristic" vs machine learning?
The wiki for metaheuristic articulates many similar ideas in machine learning yet I don't often see explicit connections between the two in literature.
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Hi, I'm fairly new to reinforcement learning. Can anyone help me to identify the reinforcement learning algorithm used in the following project?
I've tried and couldn't identify it. Any help is appreciated TIA.
This is the GitHub link for the project code
This is the link for the agent in the same.
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Guinness World Records this week presented a Stanford University-led research team with the first record for fastest DNA sequencing technique — a benchmark set using a workflow sped up by AI and accelerated computing. Achieved in five hours and two minutes, the DNA sequencing record can allow clinicians to take a blood draw from a Read article >
The post Guinness World Record Awarded for Fastest DNA Sequencing — Just 5 Hours appeared first on The Official NVIDIA Blog.
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I've been experimenting with GauGAN2, released in Nov 2021 as a follow-on to GauGAN. One new thing Nvidia added in GauGAN2 is the ability to generate a picture to match a phrase.
"A rocky stream in an ancient mossy rainforest"
It can do more than
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AI Weirdness: the strange side of machine learning
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This model classifies the different types of stars by means of artificial intelligence using Neural Designer.
https://www.neuraldesigner.com/learning/examples/star-type
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I wanted to share this blogpost on a recent paper of mine. It talks a bit through getting deep reinforcement learning to learn on a piece of hardware. If you have any questions left on the practicalities of reinforcement learning, feel free to AMA! https://www.deepmind.com/blog/article/Accelerating-fusion-science-through-learned-plasma-control
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Hey,
Is there any intuition around when we would want to learn std dev as a layer connected to our learned latent space vs as a separate Parameter space?
In some PPO implementations where the policy outputs a tensor of normal distributions (e.g. in continuous output spaces), sometimes the std dev is a learned parameter but it is not a function of the input, e.g. https://github.com/openai/spinningup/blob/038665d62d569055401d91856abb287263096178/spinup/algos/pytorch/ppo/core.py#L85
In other cases, the core network will output both the mean + std.
Thanks!
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A team of neuroscientists, engineers, and physicians showed a machine learning system for constantly automating propofol administration in a special issue of Artificial Intelligence in Medicine. The algorithm outperformed more traditional software in sophisticated, physiology-based simulations of patients using an application of deep reinforcement learning.
The software’s neural networks simultaneously learned how to maintain unconsciousness and critique the efficacy of their own actions. It also nearly matched genuine anesthesiologists’ performance when demonstrating what it would take to maintain unconsciousness given data from nine actual procedures.
The algorithm’s advances increase the feasibility for computers to maintain patient unconsciousness with no more drug than is needed. Hence, freeing up anesthesiologists for all of the other responsibilities in the operating room, such as ensuring patients remain immobile, experience no pain, remain stable, and receive adequate oxygen. Continue Reading
Paper: https://www.sciencedirect.com/science/article/pii/S0933365721002207?via%3Dihub
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I am currently reading a paper on UAV Mapping System for Agricultural Field Surveying and was curious on what AI technologies does it use (e.g., model-based diagnosis, belief networks,semantic networks, heuristic search, constraint satisfaction search, regression) or something else??
And also why is it intelligent or what aspect makes it intelligent?.
Link to paper: https://www.mdpi.com/1424-8220/17/12/2703/htm
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A team of neuroscientists, engineers, and physicians showed a machine learning system for constantly automating propofol administration in a special issue of Artificial Intelligence in Medicine. The algorithm outperformed more traditional software in sophisticated, physiology-based simulations of patients using an application of deep reinforcement learning.
The software’s neural networks simultaneously learned how to maintain unconsciousness and critique the efficacy of their own actions. It also nearly matched genuine anesthesiologists’ performance when demonstrating what it would take to maintain unconsciousness given data from nine actual procedures.
The algorithm’s advances increase the feasibility for computers to maintain patient unconsciousness with no more drug than is needed. Hence, freeing up anesthesiologists for all of the other responsibilities in the operating room, such as ensuring patients remain immobile, experience no pain, remain stable, and receive adequate oxygen. Continue Reading
Paper: https://www.sciencedirect.com/science/article/pii/S0933365721002207?via%3Dihub
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In the last two decades, the web and digital technologies have become inseparable parts of business operations globally. It is not only just using the internet and applications for enhancing workflow; businesses are relying on advanced and customised software tailor-made for their needs. They are also using diverse types of web-based services to reach out… Read More »5 Easy Steps to Choosing a Great Data Visualization Platform for Your Business
The post 5 Easy Steps to Choosing a Great Data Visualization Platform for Your Business appeared first on Data Science Central.
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Real-time rendering and photorealistic graphics used to be tall tales, but NVIDIA Omniverse has made them fact from fiction. NVIDIA’s own artists are writing new chapters in Omniverse, an accelerated 3D design platform that connects and enhances 3D apps and creative workflows, to showcase these stories. Combined with the NVIDIA Studio platform, Omniverse and Studio-validated Read article >
The post Bringing Novel Idea to Life, NVIDIA Artists Create Retro Writer’s Room in Omniverse With ‘The Storyteller’ appeared first on The Official NVIDIA Blog.
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GeForce NOW’s RTX 3080 membership is the next generation of cloud gaming. This GFN Thursday looks at one of the tier’s major benefits: ultra-low-latency streaming from the cloud. This week also brings a new app update that lets members log in via Discord, a members-only World of Warships reward and eight titles joining the GeForce Read article >
The post Performance You Can Feel: Putting GeForce NOW RTX 3080 Membership’s Ultra-Low Latency to the Test This GFN Thursday appeared first on The Official NVIDIA Blog.
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An introduction to Artificial neural networks
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In the previous article I have talked a lot about deep learning, neural networks, and types of them but today in this article we will learn…
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In many practical areas of machine learning, such as explainability, feature selection, data valuation, ensemble pruning, and federated learning, measuring relevance and attribution of various gains is a crucial topic.
For example, one may wonder: How important is a certain feature in a machine learning model’s decisions? What is the value of a single data point? Which models in an ensemble are the most valuable? Specific ways have been used to solve these concerns in various sectors. Continue Reading
Paper: https://arxiv.org/pdf/2202.05594v1.pdf
https://preview.redd.it/992wcgjmo5i81.png?width=1876&format=png&auto=webp&s=309d64e304d52baec19e8ae4c834d1727691c8a1
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Hi, i am happy to share with you the edited version of cityscapes to foggy cityscapes dataset for unsupervised domain adaptation ready to be used =)
https://github.com/fpv-iplab/Cityscapes-FoggyCityscapes
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Yesss.... A first paper in Nature today: Magnetic control of tokamak plasmas through deep reinforcement learning. After the proteins folding breakthrough, Deepmind is tackling controlled fusion through deep reinforcement learning (DRL). With the long-term promise of abundant energy without greenhouse gas emissions. What a challenge! But Deemind's Google's folks, you are our heros! Do it again! A Wired popular article.
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Scale released an interesting blogpost recently, looks like anyone can just log in and start using Scale Rapid to get data labels. Also thought the use case with the anime CycleGAN was pretty cool and had some interesting results!
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Hi ML community,
we recently released all of our papers regarding ML / AI federation, scaling and multi-processing over our website: https://www.databloom.ai/science
Its free, and we are happy to answer questions. We are also the team behind Apache Wayang, if you want to contribute, we are also happy!
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Hi all,
I am interested in finding references to up-to-date evaluation methods for generative modeling in unsupervised tasks across different model types.
I am familiar with papers such as https://arxiv.org/abs/2002.09797, https://arxiv.org/abs/2102.08921, https://arxiv.org/abs/1806.00035, which are motivated by being unable to estimate the likelihood or a lower-bound on the likelihood when using GANs.
Suppose one hoped to compare the performance across GANs, flows, and VAEs in a particular scenario. Would one of the above references, or something similar, be an approach you would consider?
Also, say you ignore GANs and compare models such as flows, VAEs, and other latent variable models. Would you consider similar approaches? I understand these models involve likelihood estimation or estimating a lower bound on the likelihood, but considering it is a lower bound for some of these models, comparing these seems off.
I appreciate any help you can provide.
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The textbook is published in print format, but a pdf version (recent draft) is available as a pdf.
Link: https://probml.github.io/pml-book/book1.html
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This post is co-authored with Jan Paul Assendorp, Thomas Lietzow, Christopher Masch, Alexander Meinert, Dr. Lars Palzer, Jan Schillemans of SIGNAL IDUNA. At SIGNAL IDUNA, a large German insurer, we are currently reinventing ourselves with our transformation program VISION2023 to become even more customer oriented. Two aspects are central to this transformation: the reorganization of […]
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Real-time feedback helps drive learning. This is especially important for designing presentations, learning new languages, and strengthening other essential skills that are critical to succeed in today’s workplace. However, many students and lifelong learners lack access to effective face-to-face instruction to hone these skills. In addition, with the rapid adoption of remote learning, educators are […]
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Time series data is widely present in our lives. Stock prices, house prices, weather information, and sales data captured over time are just a few examples. As businesses increasingly look for new ways to gain meaningful insights from time-series data, the ability to visualize data and apply desired transformations are fundamental steps. However, time-series data […]
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Easily one of the most revolutionary technologies in recent times — Blockchain is slated to disrupt so many industries that its…
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We are surrounded by artificial intelligence (AI) and machine learning (ML). Both AI and machine learning have altered the way we live and…
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Jaguar Land Rover and NVIDIA are redefining modern luxury, infusing intelligence into the customer experience. As part of its Reimagine strategy, Jaguar Land Rover announced today that it will develop its upcoming vehicles on the full-stack NVIDIA DRIVE Hyperion 8 platform, with DRIVE Orin delivering a wide spectrum of active safety, automated driving and parking Read article >
The post Reimagining Modern Luxury: NVIDIA Announces Partnership with Jaguar Land Rover appeared first on The Official NVIDIA Blog.
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Stepping deeper into the era of exascale AI, Atos gave the first look at its next-generation high-performance computer. The BullSequana XH3000 combines Atos’ patented fourth-generation liquid-cooled HPC design with NVIDIA technologies to deliver both more performance and energy efficiency. Giving users a choice of Arm or x86 computing architectures, it will come in versions using Read article >
The post Atos Previews Energy-Efficient, AI-Augmented Hybrid Supercomputer appeared first on The Official NVIDIA Blog.
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Python is a duck typing language. It means the data types of variables can change as long as the syntax […]
The post Duck-typing, scope, and investigative functions in Python appeared first on Machine Learning Mastery.
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We live in a complex world that is full of data, and it’s getting even more full every day. In 2020, the world collectively created, captured, copied, and consumed nearly 64.2 zettabytes of data and by 2025 that figure is expected to more than double to 180 zettabytes. Increasingly, companies depend on this data to… Read More »Calling All Data Scientists: Data Observability Needs You
The post Calling All Data Scientists: Data Observability Needs You appeared first on Data Science Central.
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https://thereader.mitpress.mit.edu/the-staggering-ecological-impacts-of-computation-and-the-cloud/
This supports many of the points made in this: https://kv-emptypages.blogspot.com/2021/11/the-carbon-footprint-of-machine-learning.html
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https://github.com/JoaoLages/RATransformers
I have made a package to be able to use pretrained language models on structured data.
By changing self-attention to be relation aware, you are able to pass implicit relations within the input to the model.
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Hey r/ml! I thought people here might enjoy (or possibly have a great discussion about) the latest episode in the MLOps Podcast.
In this episode, I'm speaking with Laszlo Sragner about how data scientists can write better code, how it affects real-world ML projects, and how to build an ML team. We also talk about how to break down ML problems into smaller, more manageable tasks, and a bunch of other things.
You can watch it here: https://www.youtube.com/watch?v=mtwGV-x3nSM
or listen to it here, or read some of the Q&A.
Would love to open up a discussion – what are your best practices for improving code-craft in machine learning projects?
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This model classifies iris flowers among three species (Setosa, Versicolor or Virginica) based on the length and width measurements of the sepals and petals using Neural Designer
https://www.neuraldesigner.com/learning/examples/iris-flowers-classification
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At the latest UEFA Champions League Finals, one of the world’s most anticipated annual soccer events, pop stars Marshmello, Khalid and Selena Gomez shared the stage for a dazzling opening ceremony at Portugal’s third-largest football stadium — without ever stepping foot in it. The stunning video performance took place in a digital twin of the Read article >
The post Peak Performance: Production Studio Sets the Stage for Virtual Opening Ceremony at European Football Championship appeared first on The Official NVIDIA Blog.
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